Automatic Differentiation of Algorithms: From Simulation to Optimization

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Gradients are Not All You Need (2021, background): “Automatic differentiation provides a conceptually straightforward handle for computing derivatives though these systems, and often can be applied with limited compute and memory overhead [Paszke et al., 2017, Ablin et al., 2020, Margossian, 2019, Bischof et al., 1991, Corliss et al., 2013].”
What Color Is Your Jacobian? Graph Coloring for Computing Derivatives (2005, background): “Examples of studies that investigate such methods can also be found in [33, 53, 54, 100].”
The Tapenade automatic differentiation tool: Principles, model, and specification (2013, neutral): “Several research articles describe the existing AD techniques, many in the AD conference proceedings [Corliss et al. 2002; B ¨ ucker et al. 2005; Bischof et al. 2008; Forth et al. 2012].”
Joint three-dimensional inversion of coupled groundwater flow and heat transfer based on automatic differentiation: sensitivity calculation, verification, and synthetic examples (2006, background): “Furthermore, AD is proven to be more efficient than numerical differentiation under a wide range of circumstances (Berz et al. 1996; Griewank & Corliss 1991; Corliss et al. 2002; B¨ ucker et al. 2005).”
Machine Comprehension with Discourse Relations (2015, methodology): “We compute the gradients required using Automatic Differentiation (Corliss, 2002).”